Coupling MPC with a VF, that propagates global information, optimizes action sequences for longer time horizons, while being less prominent to approximation errors than greedy action selection [1]}.
In this work, we design an actor-critic algorithm that will benefit from the integration of trajectory optimization for acquiring optimal control policies, together with the learning of stable VF approximations provided by modern deep actor-critic algorithms, e.g. soft actor-critic (SAC) [2]}. To do so, we extend the bound of [1]} by incorporating the approximation error when learning the dynamics model.
